Using Commercial Share of Wallet to Manage Vendors

ABSTRACT

Commercial size of spending wallet (“CSoSW”) is the total business spend of a business including cash but excluding bartered items. Commercial share of wallet (“CSoW”) is the portion of the spending wallet that is captured by a particular financial company. A modeling approach utilizes various data sources to provide outputs that describe a company&#39;s spend capacity. Government agencies, procurement departments, and others that patronize small businesses can use CSoW/CSoSW to determine businesses that should be awarded contracts and businesses that should be denied. CSoW/CSoSW may also be used to manage approved vendor lists.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent Ser. No. 12/103,403,filed Apr. 15, 2008 and entitled, “USING COMMERCIAL SHARE OF WALLET TOMANAGE VENDORS.” The '403 application is a continuation of U.S. patentSer. No. 11/394,166, filed Mar. 31, 2006, and entitled “USING COMMERCIALSHARE OF WALLET TO MANAGE VENDORS”. The '166 Application claims thebenefit of U.S. Provisional Application No. 60/704,428, filed Aug. 2,2005, and entitled “METHOD AND SYSTEM FOR DETERMINING COMMERCIAL SHAREOF WALLET.” The '166 Application is also a continuation-in-part of U.S.patent application Ser. No. 11/169,588, filed Jun. 30, 2005, whichissued as U.S. Pat. No. 7,912,770 on Mar. 22, 2011, and is entitled“METHOD AND APPARATUS FOR CONSUMER INTERACTION BASED ON SPEND CAPACITY.”The '588 application is a continuation-in-part of U.S. patentapplication Ser. No. 10/978,298, filed Oct. 29, 2004, which issued asU.S. Pat. No. 7,788,147 on Aug. 31, 2010, and is entitled “METHOD ANDAPPARATUS FOR ESTIMATING THE SPEND CAPACITY OF CONSUMERS.” AU the abovereferenced patents and patent applications are incorporated by referenceherein in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This disclosure generally relates to financial data processing, and inparticular it relates to credit scoring, customer profiling, customerproduct offer targeting, commercial credit behavior analysis andmodeling.

2. Background Art

For the purposes of this disclosure, middle market commercial entities,service establishments, franchises, small business corporations andpartnerships as well as business sole proprietorships will be defined asbusinesses. The term “businesses” also includes principals of a businessentity. It is axiomatic that consumers and/or businesses will tend tospend more when they have greater purchasing power. The capability toaccurately estimate a business's or a consumer's spend capacity couldtherefore allow a financial institution (such as a credit company,lender or any consumer or business services companies) to better targetpotential prospects and identify any opportunities to increase businessto business (“B2B”) or business to consumer (“B2C”) transaction volumes,without an undue increase in the risk of defaults. Attracting additionalconsumer and/or commercial spending in this manner, in turn, wouldincrease such financial institution's revenues, primarily in the form ofan increase in transaction fees and interest payments received.Consequently, a model that can accurately estimate purchasing power isof paramount interest to many financial institutions and other financialservices companies.

A limited ability to estimate spend behavior for goods and services thata business or consumer purchases has previously been available. Afinancial institution can, for example, simply monitor the balances ofits own customers' accounts. When a credit balance is lowered, thefinancial institution could then assume that the corresponding customernow has greater purchasing power. However, it is often difficult toconfirm whether the lowered balance is the result of a balance transferto another account. Such balance transfers represent no increase in thecustomer's capacity to spend, and so this simple model of customerbehavior has its flaws.

In order to achieve a complete picture of any customer's purchasingability, one must examine in detail the full range of a customer'sfinancial accounts, including credit accounts, checking and savingsaccounts, investment portfolios, and the like. However, the vastmajority of customers do not maintain all such accounts with the samefinancial institution and the access to detailed financial informationfrom other financial institutions is restricted by privacy laws,disclosure policies and security concerns.

There is limited and incomplete consumer information from credit bureausand the like at the aggregate and individual consumer levels. Sincebalance transfers are nearly impossible to consistently identify fromthe face of such records, this information has not previously beenenough to obtain accurate estimates of a consumer's actual spendingability.

Similarly, it would be useful for a financial institution to identifyspend availability for corporate consumers, such as businesses and/or aprincipal of a business entity. Such an identification would allow thefinancial institution to accurately target the corporate businessesand/or principals most likely to have spend availability, and those mostlikely to increase their plastic spend on transactional accounts relatedto the financial institution. However, there is also limited data oncorporate spend information, and identifying and predicting the size andshare of a corporate wallet is difficult.

Accordingly, there is a need for a method and apparatus for modelingindividual and corporate consumer spending behavior which addressescertain problems of existing technologies.

BRIEF SUMMARY OF THE INVENTION

A method for modeling customer behavior can be applied to both potentialand actual customers (who may be individual consumers or businesses) todetermine their spend over previous periods of time (sometimes referredto herein as the customer's size of wallet) from tradeline data sources.The share of wallet by tradeline or account type may also be determined.At the highest level, the size of wallet is represented by a consumer'sor business' total aggregate spending and the share of wallet representshow the customer uses different payment instruments.

In various embodiments, a method and apparatus for modeling consumer orbusiness behavior includes receiving individual and aggregated customerdata for a plurality of different customers. The customer data mayinclude, for example, time series tradeline data, business financialstatement data, business or consumer panel data, and internal customerdata. One or more models of consumer or business spending patterns arethen derived based on the data for one or more categories of consumer orbusiness. Categories may be based on spending levels, spending behavior,tradeline user and type of tradeline.

In various embodiments, a method and apparatus for estimating thespending levels of an individual consumer is next provided, which relieson the models of consumer behavior above. Size of wallet calculationsfor individual prospects and customers are derived from credit bureaudata sources to produce outputs using the models.

Balance transfers into credit accounts are identified based on tradelinedata according to various algorithms, and any identified balancetransfer amount is excluded from the spending calculation. Theidentification of balance transfers enables more accurate utilization ofbalance data to reflect spending.

When spending levels are reliably identified in this manner, customersmay be categorized to more effectively manage the customer relationshipand increase the profitability therefrom. For example, share of walletscores can be used as a parameter for determining whether or not toguarantee a check. The share of wallet can be used to differentiatebetween a low-risk customer who is writing more checks because hisincome has probably increased, and a high-risk customer who is writingmore checks without a corresponding increase in income or spend.

Similarly, company financial statement data can be utilized to identifyand calculate the total business spend of a company that could betransacted using a commercial credit card. A spend-like regression modelcan then be developed to estimate annual commercial size of spendingwallet values for customers and prospects of a credit network. Thisapproach relies on the High Balance Reunderwriting Unit (“HBRU”)database of commercially-underwritten businesses and the publiclyavailable tax statistics section of the IRS website, among othersources, to obtain accurate financial statement data for companiesacross various industries. Once the size of a company's spending wallethas been determined, the cardable share of the company's wallet may alsobe estimated.

Government agencies, procurement departments, and others that patronizesmall businesses can use this information to determine businesses thatshould be awarded contracts and businesses that should be denied. Thisinformation may also be used to manage approved vendor lists.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

Further aspects of the present disclosure will be more readilyappreciated upon review of the detailed description of its variousembodiments, described below, when taken in conjunction with theaccompanying drawings, of which:

FIG. 1 is a block diagram of an exemplary financial data exchangenetwork over which the processes of the present disclosure may beperformed;

FIG. 2 is a flowchart of an exemplary consumer modeling processperformed by the financial server of FIG. 1;

FIG. 3 is a diagram of exemplary categories of consumers examined duringthe process of FIG. 2;

FIG. 4 is a diagram of exemplary subcategories of consumers modeledduring the process of FIG. 2;

FIG. 5 is a diagram of financial data used for model generation andvalidation according to the process of FIG. 2;

FIG. 6 is a flowchart of an exemplary process for estimating the spendability of a consumer, performed by the financial server of FIG. 1;

FIG. 7-10 are exemplary timelines showing the rolling time periods forwhich individual customer data is examined during the process of FIG. 6;and

FIG. 11-19 are tables showing exemplary results and outputs of theprocess of FIG. 6 against a sample consumer population.

FIG. 20 is a flowchart of a method for determining commoncharacteristics across a particular category of customers according toan embodiment of the present invention.

FIG. 21 is a flowchart of a method for estimating commercial size ofspending wallet (“SoSW”) according to an embodiment of the presentinvention.

FIG. 22 is a sample financial statement that may be analyzed using themethod of FIG. 21.

FIG. 23 is a chart displaying the distribution of commercial SoSW amongOSBN HBRU businesses.

FIG. 24 is a chart displaying the median and mean commercial SoSW byindustry.

FIG. 25 is a chart displaying a sample share of wallet distributionamong HBRU accounts.

FIG. 26 is a table describing the relationship between a commercial SoSWmodel according to an embodiment of the invention and businessvariables.

FIG. 27 is a graph comparing actual commercial SoSW results to predictedcommercial SoSW estimates according to an embodiment of the presentinvention.

FIG. 28 is a graph comparing a commercial SoSW model according to anembodiment of the present invention to a perfectly random prediction.

FIG. 29 is a chart illustrating customer-level relationshipclassifications according to an embodiment of the present invention.

FIG. 30 is a chart illustrating the active number of OSBN accounts byquintile according to an embodiment of the present invention.

FIG. 31 is a table displaying customer counts in a scored output fileaccording to an embodiment of the present invention.

FIG. 32 is a block diagram of an exemplary computer system useful forimplementing the present invention.

DETAILED DESCRIPTION OF THE INVENTION

While specific configurations and arrangements are discussed, it shouldbe understood that this is done for illustrative purposes only. A personskilled in the pertinent art will recognize that other configurationsand arrangements can be used without departing from the spirit and scopeof the present invention. It will be apparent to a person skilled in thepertinent art that this invention can also be employed in a variety ofother applications.

In an aspect of this invention, the term “business” will refer tonon-publicly traded business entities, such as middle market commercialentities, franchises, small business corporations and partnerships, andsole proprietorships, as well as principals of these business entities.One of skill in the pertinent art will recognize that the presentinvention may be used in reference to consumers, businesses, andpublicly traded companies without departing from the spirit and scope ofthe present invention.

As used herein, the following terms shall have the following meanings. Aconsumer refers to an individual consumer and/or a small business. Atrade or tradeline refers to a credit or charge vehicle issued to anindividual customer by a credit grantor. Types of tradelines include,for example and without limitation, bank loans, credit card accounts,retail cards, personal lines of credit and car loans/leases. Forpurposes here, use of the term credit card shall be construed to includecharge cards except as specifically noted. Tradeline data describes thecustomer's account status and activity, including, for example, names ofcompanies where the customer has accounts, dates such accounts wereopened, credit limits, types of accounts, balances over a period of timeand summary payment histories. Tradeline data is generally available forthe vast majority of actual consumers. Tradeline data, however, does notinclude individual transaction data, which is largely unavailablebecause of consumer privacy protections. Tradeline data may be used todetermine both individual and aggregated consumer spending patterns, asdescribed herein.

Consumer panel data measures consumer spending patterns from informationthat is provided by, typically, millions of participating consumerpanelists. Such consumer panel data is available through variousconsumer research companies, such as comScore Networks, Inc. of Reston,Va. Consumer panel data may typically include individual consumerinformation such as credit risk scores, credit card application data,credit card purchase transaction data, credit card statement views,tradeline types, balances, credit limits, purchases, balance transfers,cash advances, payments made, finance charges, annual percentage ratesand fees charged. Such individual information from consumer panel data,however, is limited to those consumers who have participated in theconsumer panel, and so such detailed data may not be available for allconsumers.

Although embodiments of the invention herein may be described asrelating to individual consumers, one of skill in the pertinent art(s)will recognize that they can also apply to small businesses andorganizations or principals thereof without departing from the spiritand scope of the present invention.

I. CONSUMER PANEL DATA AND MODEL DEVELOPMENT/VALIDATION

Technology advances have made it possible to store, manipulate and modellarge amounts of time series data with minimal expenditure on equipment.As will now be described, a financial institution may leverage thesetechnological advances in conjunction with the types of consumer datapresently available in the marketplace to more readily estimate thespend capacity of potential and actual customers. A reliable capabilityto assess the size of a consumer's wallet is introduced in whichaggregate time series and raw tradeline data are used to model consumerbehavior and attributes, and identify categories of consumers based onaggregate behavior. The use of raw trade-line time series data, andmodeled consumer behavior attributes, including but not limited to,consumer panel data and internal consumer data, allows actual consumerspend behavior to be derived from point in time balance information.

In addition, the advent of consumer panel data provided through internetchannels provides continuous access to actual consumer spend informationfor model validation and refinement. Industry data, including consumerpanel information having consumer statement and individual transactiondata, may be used as inputs to the model and for subsequent verificationand validation of its accuracy. The model is developed and refined usingactual consumer information with the goals of improving the customerexperience and increasing billings growth by identifying and leveragingincreased consumer spend opportunities.

A credit provider or other financial institution may also make use ofinternal proprietary customer data retrieved from its stored internalfinancial records. Such internal data provides access to even moreactual customer spending information, and may be used in thedevelopment, refinement and validation of aggregated consumer spendingmodels, as well as verification of the models' applicability to existingindividual customers on an ongoing basis.

While there has long been market place interest in understanding spendto align offers with consumers and assign credit line size, the holisticapproach of using a size of wallet calculation across customerslifecycles (that is, acquisitions through collections) has notpreviously been provided. The various data sources outlined aboveprovide the opportunity for unique model logic development anddeployment, and as described in more detail in the following, variouscategories of consumers may be readily identified from aggregate andindividual data. In certain embodiments of the processes disclosedherein, the models may be used to identify specific types of consumers,nominally labeled ‘transactors’ and ‘revolvers,’ based on aggregatespending behavior, and to then identify individual customers andprospects that fall into one of these categories. Consumers falling intothese categories may then be offered commensurate purchasing incentivesbased on the model's estimate of consumer spending ability.

Referring now to FIGS. 1-19, wherein similar components of the presentdisclosure are referenced in like manner, various embodiments of amethod and system for estimating the purchasing ability of consumerswill now be described in detail.

Turning now to FIG. 1, there is depicted an exemplary computer network100 over which the transmission of the various types of consumer data asdescribed herein may be accomplished, using any of a variety ofavailable computing components for processing such data in the mannersdescribed below. Such components may include an institution computer102, which may be a computer, workstation or server, such as thosecommonly manufactured by IBM, and operated by a financial institution orthe like. The institution computer 102, in turn, has appropriateinternal hardware, software, processing, memory and networkcommunication components that enables it to perform the functionsdescribed here, including storing both internally and externallyobtained individual or aggregate consumer data in appropriate memory andprocessing the same according to the processes described herein usingprogramming instructions provided in any of a variety of useful machinelanguages.

The institution computer 102 may in turn be in operative communicationwith any number of other internal or external computing devices,including for example components 104, 106, 108, and 110, which may becomputers or servers of similar or compatible functional configuration.These components 104-110 may gather and provide aggregated andindividual consumer data, as described herein, and transmit the same forprocessing and analysis by the institution computer 102. Such datatransmissions may occur for example over the Internet or by any otherknown communications infrastructure, such as a local area network, awide area network, a wireless network, a fiber-optic network, or anycombination or interconnection of the same. Such communications may alsobe transmitted in an encrypted or otherwise secure format, in any of awide variety of known manners.

Each of the components 104-110 may be operated by either common orindependent entities. In one exemplary embodiment, which is not to belimiting to the scope of the present disclosure, one or more suchcomponents 104-110 may be operated by a provider of aggregate andindividual consumer tradeline data, an example of which includesservices provided by Experian Information Solutions, Inc. of Costa Mesa,Calif. (“Experian”). Tradeline level data preferably includes up to 24months or more of balance history and credit attributes captured at thetradeline including information about accounts as reported by variouscredit grantors, which in turn may be used to derive a broad view ofactual aggregated consumer behavioral spending patterns.

Alternatively, or in addition thereto, one or more of the components104-110 may likewise be operated by a provider of individual andaggregate consumer panel data, such as commonly provided by comScoreNetworks, Inc. of Reston, Va. (“comScore”). Consumer panel data providesmore detailed and specific consumer spending information regardingmillions of consumer panel participants, who provide actual spend datato collectors of such data in exchange for various inducements. The datacollected may include any one or more of credit risk, scores, onlinecredit card application data, online credit card purchase transactiondata, online credit card statement views, credit trade type and creditissuer, credit issuer code, portfolio level statistics, credit bureaureports, demographic data, account balances, credit limits, purchases,balance transfers, cash advances, payment amounts, finance charges,annual percentage interest rates on accounts, and fees charged, all atan individual level for each of the participating panelists. In variousembodiments, this type of data is used for model development, refinementand verification. This type of data is further advantageous overtradeline level data alone for such purposes, since such detailedinformation is not provided at the tradeline level. While such detailedconsumer panel data can be used alone to generate a model, it may not bewholly accurate with respect to the remaining marketplace of consumersat large without further refinement. Consumer panel data may also beused to generate aggregate consumer data for model derivation anddevelopment.

Additionally, another source of inputs to the model may be internalspend and payment history of the institution's own customers. From suchinternal data, detailed information at the level of specificity as theconsumer panel data may be obtained and used for model development,refinement and validation, including the categorization of consumersbased on identified transactor and revolver behaviors.

Turning now to FIG. 2, there is depicted a flowchart of an exemplaryprocess 200 for modeling aggregate consumer behavior in accordance withthe present disclosure. The process 200 commences at step 202 whereinindividual and aggregate consumer data, including time-series tradelinedata, consumer panel data and internal customer financial data, isobtained from any of the data sources described previously as inputs forconsumer behavior models. In certain embodiments, the individual andaggregate consumer data may be provided in a variety of different dataformats or structures and consolidated to a single useful format orstructure for processing.

Next, at step 204, the individual and aggregate consumer data isanalyzed to determine consumer spending behavior patterns. One ofordinary skill in the art will readily appreciate that the models mayinclude formulas that mathematically describe the spending behavior ofconsumers. The particular formulas derived will therefore highly dependon the values resulting from customer data used for derivation, as willbe readily appreciated. However, by way of example only and based on thedata provided, consumer behavior may be modeled by first dividingconsumers into categories that may be based on account balance levels,demographic profiles, household income levels or any other desiredcategories. For each of these categories in turn, historical accountbalance and transaction information for each of the consumers may betracked over a previous period of time, such as one to two years.Algorithms may then be employed to determine formulaic descriptions ofthe distribution of aggregate consumer information over the course ofthat period of time for the population of consumers examined, using anyof a variety of known mathematical techniques. These formulas in turnmay be used to derive or generate one or more models (step 206) for eachof the categories of consumers using any of a variety of available trendanalysis algorithms. The models may yield the following types ofaggregated consumer information for each category: average balances,maximum balances, standard deviation of balances, percentage of balancesthat change by a threshold amount, and the like.

Finally, at step 208, the derived models may be validated andperiodically refined using internal customer data and consumer paneldata from sources such as comScore. In various embodiments, the modelmay be validated and refined over time based on additional aggregatedand individual consumer data as it is continuously received by aninstitution computer 102 over the network 100. Actual customertransaction level information and detailed consumer information paneldata may be calculated and used to compare actual consumer spend amountsfor individual consumers (defined for each month as the differencebetween the sum of debits to the account and any balance transfers intothe account) and the spend levels estimated for such consumers using theprocess 200 above. If a large error is demonstrated between actual andestimated amounts, the models and the formulas used may be manually orautomatically refined so that the error is reduced. This allows for aflexible model that has the capability to adapt to actual aggregatedspending behavior as it fluctuates over time.

As shown in the diagram 300 of FIG. 3, a population of consumers forwhich individual and/or aggregated data has been provided may be dividedfirst into two general categories for analysis, for example, those thatare current on their credit accounts (representing 1.72 millionconsumers in the exemplary data sample size of 1.78 million consumers)and those that are delinquent (representing 0.06 million of suchconsumers). In one embodiment, delinquent consumers may be discardedfrom the populations being modeled.

In further embodiments, the population of current consumers is thensubdivided into a plurality of further categories based on the amount ofbalance information available and the balance activity of such availabledata. In the example shown in the diagram 300, the amount of balanceinformation available is represented by string of ‘+’ ‘0’ and ‘?’characters. Each character represents one month of available data, withthe rightmost character representing the most current months and theleftmost character representing the earliest month for which data isavailable. In the example provided in FIG. 3, a string of six charactersis provided, representing the six most recent months of data for eachcategory. The “+” character represents a month in which a credit accountbalance of the consumer has increased. The “0” character may representmonths where the account balance is zero. The “?” character representsmonths for which balance data is unavailable. Also provided the diagramis number of consumers fallen to each category and the percentage of theconsumer population they represent in that sample.

In further embodiments, only certain categories of consumers may beselected for modeling behavior. The selection may be based on thosecategories that demonstrate increased spend on their credit balancesover time. However, it should be readily appreciated that othercategories can be used. FIG. 3 shows the example of two categories ofselected consumers for modeling in bold. These groups show theavailability of at least the three most recent months of balance dataand that the balances increased in each of those months.

Turning now to FIG. 4, therein is depicted an exemplary diagram 400showing sub-categorization of the two categories of FIG. 3 in bold thatare selected for modeling. In the embodiment shown, the sub-categoriesmay include: consumers having a most recent credit balance less than$400; consumers having a most recent credit balance between $400 and$1600; consumers having a most recent credit balance between $1600 and$5000; consumers whose most recent credit balance is less than thebalance of, for example, three months ago; consumers whose maximumcredit balance increase over, for example, the last twelve monthsdivided by the second highest maximum balance increase over the sameperiod is less than 2; and consumers whose maximum credit balanceincrease over the last twelve months divided by the second highestmaximum balance increase is greater than 2. It should be readilyappreciated that other subcategories can be used. Each of thesesub-categories is defined by their last month balance level. The numberof consumers from the sample population (in millions) and the percentageof the population for each category are also shown in FIG. 4.

There may be a certain balance threshold established, wherein if aconsumer's account balance is too high, their behavior may not bemodeled, since such consumers are less likely to have sufficientspending ability. Alternatively, or in addition thereto, consumershaving balances above such threshold may be sub-categorized yet again,rather than completely discarded from the sample. In the example shownin FIG. 4, the threshold value may be $5000, and only those havingparticular historical balance activity may be selected, i.e. thoseconsumers whose present balance is less than their balance three monthsearlier, or whose maximum balance increase in the examined period meetscertain parameters. Other threshold values may also be used and may bedependent on the individual and aggregated consumer data provided.

As described in the foregoing, the models generated in the process 200may be derived, validated and refined using tradeline and consumer paneldata. An example of tradeline data 500 from Experian and consumer paneldata 502 from comScore are represented in FIG. 5. Each row of the data500, 502 represents the record of one consumer and thousands of suchrecords may be provided at a time. The statement 500 shows thepoint-in-time balance of consumers accounts for three successive months(Balance 1, Balance 2 and Balance 3). The data 502 shows each consumer'spurchase volume, last payment amount, previous balance amount andcurrent balance. Such information may be obtained, for example, by pagescraping the data (in any of a variety of known manners usingappropriate application programming interfaces) from an Internet website or network address at which the data 502 is displayed. Furthermore,the data 500 and 502 may be matched by consumer identity and combined byone of the data providers or another third party independent of thefinancial institution. Validation of the models using the combined data500 and 502 may then be performed, and such validation may beindependent of consumer identity.

Turning now to FIG. 6, therein is depicted an exemplary process 600 forestimating the size of an individual consumer's spending wallet. Uponcompletion of the modeling of the consumer categories above, the process600 commences with the selection of individual consumers or prospects tobe examined (step 602). An appropriate model derived during the process200 will then be applied to the presently available consumer tradelineinformation in the following manner to determine, based on the resultsof application of the derived models, an estimate of a consumer's sizeof wallet. Each consumer of interest may be selected based on theirfalling into one of the categories selected for modeling describedabove, or may be selected using any of a variety of criteria.

The process 600 continues to step 604 where, for a selected consumer, apaydown percentage over a previous period of time is estimated for eachof the consumer's credit accounts. In one embodiment, the paydownpercentage is estimated over the previous three-month period of timebased on available tradeline data, and may be calculated according tothe following formula:

Pay-down %=(The sum of the last three months payments from theaccount)/(The sum of three month balances for the account based ontradeline data).

The paydown percentage may be set to, for example, 2%, for any consumerexhibiting less than a 5% paydown percentage, and may be set to 100% ifgreater than 80%, as a simplified manner for estimating consumerspending behaviors on either end of the paydown percentage scale.

Consumers that exhibit less than a 50% paydown during this period may becategorized as revolvers, while consumers that exhibit a 50% paydown orgreater may be categorized as transactors. These categorizations may beused to initially determine what, if any, purchasing incentives may beavailable to the consumer, as described later below.

The process 600, then continues to step 606, where balance transfers fora previous period of time are identified from the available tradelinedata thr the consumer. The identification of balance transfers areessential since, although tradeline data may reflect a higher balance ona credit account over time, such higher balance may simply be the resultof a transfer of a balance into the account, and are thus not indicativeof a true increase in the consumer's spending. It is difficult toconfirm balance transfers based on tradeline data since the informationavailable is not provided on a transaction level basis. In addition,there are typically lags or absences of reporting of such values ontradeline reports.

Nonetheless, marketplace analysis using confirmed consumer panel andinternal customer financial records has revealed reliable ways in whichbalance transfers into an account may be identified from imperfectindividual tradeline data alone. Three exemplary reliable methods foridentifying balance transfers from credit accounts, each which is basedin part on actual consumer data sampled, are as follows. It should bereadily apparent that these formulas in this form are not necessary forall embodiments of the present process and may vary based on theconsumer data used to derive them.

A first rule identifies a balance transfer for a given consumer's creditaccount as follows. The month having the largest balance increase in thetradeline data, and which satisfies the following conditions, may beidentified as a month in which a balance transfer has occurred:

-   -   The maximum balance increase is greater than twenty times the        second maximum balance increase for the remaining months of        available data;    -   The estimated pay-down percent calculated at step 306 above is        less than 40%; and    -   The largest balance increase is greater than $1000 based on the        available data.

A second rule identifies a balance transfer for a given consumer'scredit account in any month where the balance is above twelve times theprevious month's balance and the next month's balance differs by no morethan 20%.

A third rule identifies a balance transfer for a given consumer's creditaccount in any month where:

-   -   the current balance is greater than 1.5 times the previous        month's balance;    -   the current balance minus the previous month's balance is        greater than $4500; and    -   the estimated pay-down percent from step 306 above is less than        30%.

The process 600 then continues to step 608, where consumer spending oneach credit account is estimated over the next, for example, three monthperiod. In estimating consumer spend, any spending for a month in whicha balance transfer has been identified from individual tradeline dataabove is set to zero for purposes of estimating the size of theconsumer's spending wallet, reflecting the supposition that no realspending has occurred on that account. The estimated spend for each ofthe three previous months may then be calculated as follows:

Estimated spend=(the current balance−the previous month's balance+(theprevious month's balance the estimated pay-down % from step 604 above).

The exact form of the formula selected may be based on the category inwhich the consumer is identified from the model applied, and the formulais then computed iteratively for each of the three months of the firstperiod of consumer spend.

Next, at step 610 of the process 600, the estimated spend is thenextended over, for example, the previous three quarterly or three-monthperiods, providing a most-recent year of estimated spend for theconsumer.

Finally, at step 612, this in turn may be used to generate a pluralityof final outputs for each consumer account (step 314). These may beprovided in an output file that may include a portion or all of thefollowing exemplary information, based on the calculations above andinformation available from individual tradeline data:

(i) size of previous twelve month spending wallet; (ii) size of spendingwallet for each of the last four quarters; (iii) total number ofrevolving cards, revolving balance, and average pay down percentage foreach; (iv) total number of transacting cards, and transacting balancesfor each; (v) the number of balance transfers and total estimated amountthereof; (vi) maximum revolving balance amounts and associated creditlimits; and (vii) maximum transacting balance and associated creditlimit.

After step 612, the process 600 ends with respect to the examinedconsumer. It should be readily appreciated that the process 600 may berepeated for any number of current customers or consumer prospects.

Referring now to FIGS. 7-10, therein is depicted illustrative diagrams700-1000 of how such estimated spending is calculated in a rollingmanner across each previous three month (quarterly) period. In FIG. 7,there is depicted a first three month period (i.e., the most recentprevious quarter) 702 on a timeline 710. As well, there is depicted afirst twelve-month period 704 on a timeline 708 representing the lasttwenty-one months of point-in-time account balance information availablefrom individual tradeline data for the consumer's account. Each month'sbalance for the account is designated as “B#.” B1-B12 represent actualaccount balance information available over the past twelve months forthe consumer. B13-B21 represent consumer balances over consecutive,preceding months.

In accordance with the diagram 700, spending in each of the three monthsof the first quarter 702 is calculated based on the balance valuesB1-B12, the category of the consumer based on consumer spending modelsgenerated in the process 200, and the formulas used in steps 604 and606.

Turning now to FIG. 8, there is shown a diagram 800 illustrating thebalance information used for estimating spending in a second previousquarter 802 using a second twelve-month period of balance information804. Spending in each of these three months of the second previousquarter 802 is based on known balance information B4-B15.

Turning now to FIG. 9, there is shown a diagram 900 illustrating thebalance information used for estimating spending in a third successivequarter 902 using a third twelve-month period of balance information904. Spending in each of these three months of the third previousquarter 902 is based on known balance information B7-B18.

Turning now to FIG. 10, there is shown a diagram 1000 illustrating thebalance information used for estimating spending in a fourth previousquarter 1002 using a fourth twelve-month period of balance information1004. Spending in each of these three months of the fourth previousquarter 1002 is based on balance information B10-B21.

It should be readily appreciated that as the rolling calculationsproceed, the consumer's category may change based on the outputs thatresult, and, therefore, different formula corresponding to the newcategory may be applied to the consumer for different periods of time.The rolling manner described above maximizes the known data used forestimating consumer spend in a previous twelve month period 1006.

Based on the final output generated for the customer, commensuratepurchasing incentives may be identified and provided to the consumer,for example, in anticipation of an increase in the consumer's purchasingability as projected by the output file. In such cases, consumers ofgood standing, who are categorized as transactors with a projectedincrease in purchasing ability, may be offered a lower financing rate onpurchases made during the period of expected increase in theirpurchasing ability, or may be offered a discount or rebate fortransactions with selected merchants during that time.

In another example, and in the case where a consumer is a revolver, suchconsumer with a projected increase in purchasing ability may be offereda lower annual percentage rate on balances maintained on their creditaccount.

Other like promotions and enhancements to consumers' experiences arewell known and may be used within the processes disclosed herein.

Various statistics for the accuracy of the processes 200 and 600 areprovided in FIGS. 11-18, for which a consumer sample was analyzed by theprocess 200 and validated using 24 months of historic actual spend data.The table 1100 of FIG. 11 shows the number of consumers having a balanceof $5000 or more for whom the estimated paydown percentage (calculatedin step 604 above) matched the actual paydown percentage (as determinedfrom internal transaction data and external consumer panel data).

The table 1200 of FIG. 12 shows the number of consumers having a balanceof $5000 or more who were expected to be transactors or revolvers, andwho actually turned out to be transactors and revolvers based on actualspend data. As can be seen, the number of expected revolvers who turnedout to be actual revolvers (80539) was many times greater than thenumber of expected revolvers who turned out to be transactors (1090).Likewise, the number of expected and actual transactors outnumbered bynearly four-to-one the number of expected transactors that turned out tobe revolvers.

The table 1300 of FIG. 13 shows the number of estimated versus actualinstances in the consumer sample of when there occurred a balancetransfer into an account. For instance, in the period sampled, therewere 148,326 instances where no balance transfers were identified instep 606 above, and for which a comparison of actual consumer datashowed there were in fact no balance transfers in. This compares to only9,534 instances where no balance transfers were identified in step 606,but there were in fact actual balance transfers.

The table 1400 of FIG. 14 shows the accuracy of estimated spending (insteps 608-612) versus actual spending for consumers with accountbalances (at the time this sample testing was performed) greater than$5000. As can be seen, the estimated spending at each spending levelmost closely matched the same actual spending level than for any otherspending level in nearly all instances.

The table 1500 of FIG. 15 shows the accuracy of estimated spending (insteps 608-612) versus actual spending for consumers having most recentaccount balances between $1600 and $5000. As can be readily seen, theestimated spending at each spending level most closely matched the sameactual spending level than for any other spending level in allinstances.

The table 1600 of FIG. 16 shows the accuracy of estimated spendingversus actual spending for all consumers in the sample. As can bereadily seen, the estimated spending at each spending level most closelymatched the same actual spending level than for any other actualspending level in all instances.

The table 1700 of FIG. 17 shows the rank order of estimated versusactual spending for all consumers in the sample. This table 1700 readilyshows that the number of consumers expected to be in the bottom 10% ofspending most closely matched the actual number of consumers in thatcategory, by 827,716 to 22,721. The table 1700 further shows that thenumber of consumers expected to be in the top 10% of spenders mostclosely matched the number of consumers who were actually in the top10%, by 71,773 to 22,721.

The table 1800 of FIG. 18 shows estimated versus actual annual spendingfor all consumers in the sample over the most recent year of availabledata. As can be readily seen, the expected number of consumers at eachspending level most closely matched the same actual spending level thanany other level in all instances.

Finally, the table 1900 of FIG. 19 shows the rank order of estimatedversus actual total annual spending for all the consumers over the mostrecent year of available data. Again, the number of expected consumersin each rank most closely matched the actual rank than any other rank.

Prospective customer populations used for modeling and/or laterevaluation may be provided from any of a plurality of availablemarketing groups, or may be culled from credit bureau data, targetedadvertising campaigns or the like. Testing and analysis may becontinuously performed to identify the optimal placement and requiredfrequency of such sources for using the size of spending walletcalculations. The processes described herein may also be used to developmodels for predicting a size of wallet for an individual consumer in thefuture.

Institutions adopting the processes disclosed herein may expect to morereadily and profitably identify opportunities for prospect and customerofferings, which in turn provides enhanced experiences across all partsof a customer's lifecycle. In the case of a credit provider, accurateidentification of spend opportunities allows for rapid provisioning ofcard member offerings to increase spend that, in turn, results inincreased transaction fees, interest charges and the like. The carefulselection of customers to receive such offerings reduces the incidenceof fraud that may occur in less disciplined card member incentiveprograms. This, in turn, reduces overall operating expenses forinstitutions.

II. MODEL OUTPUT FOR INDIVIDUAL CONSUMERS

As mentioned above, the process described may also be used to developmodels for predicting a size of wallet for an individual consumer in thefuture. The capacity a consumer has for spending in a variety ofcategories is the share of wallet. The model used to determine share ofwallet for particular spend categories using the processes describedherein is the share of wallet (“SoW”) model. The SoW model providesestimated data and/or characteristics information that is moreindicative of consumer spending power than typical credit bureau data orscores. The SoW model may output, with sufficient accuracy, data that isdirectly related to the spend capacity of an individual consumer. One ofskill in the art will recognize that any one or combination of thefollowing data types, as well as other data types, may be output by theSoW model without altering the spirit and scope of the presentinvention.

The size of a consumer's twelve-month spending wallet is an exampleoutput of the SoW model. This type of data is typically output as anactual or rounded dollar amount. The size of a consumer's spendingwallet for each of several consecutive quarters, for example, the mostrecent four quarters, may also be output.

The SoW model output may include the total number of revolving cardsheld by a consumer, the consumer's revolving balance, and/or theconsumer's average pay-down percentage of the revolving cards. Themaximum revolving balance and associated credit limits can be determinedfor the consumer, as well as the size of the consumer's revolvingspending.

Similarly, the SoW model output may include the total number of aconsumer's transacting cards and/or the consumer's transacting balance.The SoW model may additionally output the maximum transacting balance,the associated credit limit, and/or the size of transactional spendingof the consumer.

These outputs, as well as any other outputs from the SoW model, may beappended to data profiles of a company's customers and prospects. Thisenhances the company's ability to make decisions involving prospecting,new applicant evaluation, and customer relationship management acrossthe customer lifecycle.

Additionally or alternatively, the output of the model can be calculatedto equal a SoW score, much like credit bureau data is used to calculatea credit rating. Credit bureau scores are developed from data availablein a consumer's file, such as the amount of lines of credit, paymentperformance, balance, and number of tradelines. This data is used tomodel the risk of a consumer over a period of time using statisticalregression analysis. Those data elements that are found to be indicativeof risk are weighted and combined to determine the credit score. Forexample, each data element may be given a score, with the final creditscore being the sum of the data element scores.

A SoW score, based on the SoW model, may provide a higher level ofpredictability regarding spend capacity and creditworthiness. The SoWscore can focus, for example, on total spend, plastic spend and/or aconsumer's spending trend. Using the processes described above, balancetransfers are factored out of a consumer's spend capacity. Further, whencorrelated with a risk score, the SoW score may provide more insightinto behavior characteristics of relatively low-risk consumers andrelatively high-risk consumers.

The SoW score may be structured in one of several ways. For instance,the score may be a numeric score that reflects a consumer's spend invarious ranges over a given time period, such as the last quarter oryear. As an example, a score of 5000 might indicate that a consumerspent between $5000 and $6000 in the given time period.

Alternatively or additionally, the score may include a range of numbersor a numeric indicator, such as an exponent, that indicates the trend ofa consumer's spend over a given time period. For example, a trend scoreof +4 may indicate that a consumer's spend has increased over theprevious 4 months, while a trend score of ˜4 may indicate that aconsumer's spend has decreased over the previous 4 months.

In addition to determining an overall SoW score, the SoW model outputsmay each be given individual scores and used as attributes forconsideration in credit score development by, for example, traditionalcredit bureaus. As discussed above, credit scores are traditionallybased on information in a customer's credit bureau file. Outputs of theSoW model, such as balance transfer information, spend capacity andtrend, and revolving balance information, could be more indicative ofrisk than some traditional data elements. Therefore, a company may usescored SoW outputs in addition to or in place of traditional dataelements when computing a final credit score. This information may becollected, analyzed, and/or summarized in a scorecard. This would beuseful to, for example and without limitation, credit bureaus, majorcredit grantors, and scoring companies, such as Fair Isaac Corporationof Minneapolis, Minn.

The SoW model outputs for individual consumers or small businesses canalso be used to develop various consumer models to assist in directmarketing campaigns, especially targeted direct marketing campaigns. Forexample, “best customer” or “preferred customer” models may be developedthat correlate characteristics from the SoW model outputs, such asplastic spend, with certain consumer groups. If positive correlationsare identified, marketing and customer relationship managementstrategies may be developed to achieve more effective results.

In an example embodiment, a company may identify a group of customers asits “best customers.” The company can process information about thosecustomers according to the SoW model. This may identify certain consumercharacteristics that are common to members of the best customer group.The company can then profile prospective customers using the SoW model,and selectively target those who have characteristics in common with thecompany's best consumer model.

FIG. 20 is a flowchart, of a method 2000 for using model outputs toimprove customer profiling. In step 2002, customers are segmented intovarious categories. Such categories may include, for example and withoutlimitation, best customers, profitable customers, marginal customers,and other customers.

In step 2004, model outputs are created for samples of customers fromeach category. The customers used in step 2004 are those for whomdetailed information is known.

In step 2006, it is determined whether there is any correlation betweenparticular model outputs and the customer categories.

Alternatively, the SoW model can be used to separate existing customerson the basis of spend capacity. This allows separation into groups basedon spend capacity. A company can then continue with method 2000 foridentifying correlations, or the company may look to non-credit-relatedcharacteristics of the consumers in a category for correlations.

If a correlation is found, the correlated model output(s) is deemed tobe characteristic and/or predictive of the related category ofcustomers. This output can then be considered when a company looks forcustomers who fit its best customer model.

III. MODELING AND OUTPUTS FOR COMMERCIAL CONSUMERS

Commercial size of spending wallet (“SoSW”) may also be predicted.Commercial SoSW is the total business-related spending of a companyincluding cash but excluding bartered items. In order to determinecommercial SoSW, data is needed from sources other than consumer creditbureaus. This is because, according to market studies, approximately 7%of small business spending occurs on plastic. Thus, only a small portionof total business spend would be captured by consumer credit bureaus.Company financial statements, however, provide a comprehensive summaryof business spend.

Company financial statement data may be used in a top-down method toestimate commercial SoSW. FIG. 21 is a flowchart of an example methodfor estimating commercial SoSW. In step 2102, company financialstatement data is obtained. The company of interest may be a customerand/or prospect in a credit network. An example credit network is OPEN:The Small Business Network (“OSBN”) from American Express. Althoughcredit network companies will be referred to herein as OSBN companies,one of skill in the pertinent art will recognize that any credit networkmay be used without departing from the spirit and scope of the presentinvention. The company financial statement data may be obtained from,for example, the High Balance Reunderwriting Unit (“HBRU”) database ofcommercially underwritten OSBN businesses. The HBRU database includesdata on high-spending OSBN customers that are underwritten at leastannually. The database also includes business financial statements,which are a standard requirement of the underwriting process. Usuallycovering 12 months, these financial statements provide detailed expenseinformation that can be used to assess potential plastic, or creditcard, spend. Also included in the database are over approximately 33,000underwriting events for approximately 16,000 unique OSBN businesses.

Detailed operating expenses (“OpEx”) costs from the HBRU database areavailable in hard copy only, making it difficult to electronicallydifferentiate different types of spend, such as cardable (spend thatcould be put on plastic) and uncardable (spend that could not be put onplastic). An example source for electronic company financial statementdata is the tax statistics section of the Internal Revenue Service(“IRS”) website. This section of the IRS website includes businesssummary statistics based on a stratified, weighted sample ofapproximately 500,000 unaudited company tax returns and financialstatements. Available fields in the IRS website include OpEx details,which allow for electronic distinction between cardable and uncardablespend. These summaries are available at the industry and/or legalstructure level. The industry grouping is based on the North AmericanIndustry Classification System (“NAICS”), which replaced the U.S.Standard Industrial Classification (“SIC”) system.

Additional sources of company financial statement data include, forexample and without limitation, trade credit data from the Equifax SmallBusiness Enterprise (“SBE”) database, produced by Equifax Inc. ofAtlanta, Ga.; the Experian Business Information Solutions (“BIS”)database produced by Experian of Costa Mesa, Calif.; and the Dun &Bradstreet database, produced by Dun & Bradstreet Corp. of Short Hills,N.J. Trade credit data is credit provided by suppliers to merchants atthe supplier offices. Trade credit has been associated with variousrepayment options, including, for example, a 2% discount if paid back tothe supplier in 10 days, with the net amount due within 30 days. Such arepayment term is usually referred to as 2/10 net 30.

In step 2104, total business spend that could be transacted using acommercial credit card is identified and calculated. FIG. 22 is a samplefinancial statement that may be analyzed using the commercial SoSWmodel. The SoSW model for a particular business considers at least twocomponents: cost of goods sold (“CoGS”) and operating expenses (“OpEx”).For purposes of this application, it is assumed that 100% of CoGS spendcan be converted to plastic. Each OpEx component is classified as“cardable” or “uncardable”. These components may be distinguished in thestatement, as is shown in the example of FIG. 22. Only the cardable OpExis included in the commercial SoSW calculation. The total SoSW for aparticular business can be calculated by adding the CoGS and thecardable OpEx:

SoSW=CoGS+Cardable OpEx

Thus, according to the sample financial statement in FIG. 22, the CoGSequals $5,970,082, the total OpEx equals $285,467, and the cardable OpExequals $79,346 (28% of total OpEx). The total SoSW for this businessthus equals $6,049,428. Once the total SoSW has been calculated, method2100 proceeds to step 2106.

In step 2106, a spend-like regression model is used to estimate annualcommercial SoSW value for OSBN customers and prospects. Theindustry-based summaries from the IRS website, for example, may be usedto calculate a cardable OpEx percentage for each combination of industryand legal structure. This will be referred to herein as the cardableOpEx ratio. Based on the industry and legal structure of credit networkcustomers in, for example, the HBRU database, the relevant cardable OpExratio is applied.

Industry-level commercial SoSW is calculated using the given cost ofgoods sold, total operating expenses, and the cardable OpEx ratio asderived from, for example, the IRS data:

SoSW=CoGS+(Total OpEx*Cardable OpEx Ratio)

These elasticities within the industries can then be analyzed to derivebusiness-level estimations of SoSW. FIG. 23 displays the distribution ofcommercial SoSW estimates among the OSBN HBRU businesses. This analysisis based on OSBN underwriting events over approximately 2.5 years,resulting in 16,337 underwriting events across 8,657 unique OSBNbusinesses.

Commercial SoSW differs significantly by industry. As shown in FIG. 24,most industries include a small percentage of high-potential businessesthat drive a large discrepancy between the mean and median SoSW values.

Commercial SoSW represents overall annual cardable expenditures. Asdiscussed above, share of wallet (“SoW”) represents the portion of thetotal spending wallet that is allocated towards, for example, aparticular financial institution. Commercial share of wallet (SoW) canbe measured by dividing annual OSBN spend (from the global riskmanagement system (“GRMS”)) into commercial SoSW. As shown in FIG. 25,over 51% of HBRU businesses have a commercial SoW of less than 10%. Thisillustrates the magnitude of the opportunity to capture additionalspend.

FIG. 26 is a table that describes the relationship between thecommercial SoSW model and business variables. This information is basedon Dun & Bradstreet data, and the adjusted R² value for the dataanalyzed is 0.3456. The commercial SoSW model takes into consideration,for example and without limitation, annual sales amount of the company,number of employees in the company, highest credit amount of the companywithin the previous 13 months, total dollar amount of satisfactoryfinancial experiences by the company over the previous 1$ months, and afinancial stress score percentile of the company, wherein a percentileof 0 indicates highest risk, and a percentile of 100 indicates lowestrisk. Annual sales amount, number of employees, and highest creditamount within the last 13 months all have a positive linear effect on acompany's commercial SoSW. The total dollar amount of satisfactoryfinancial experiences over the last 13 months has a positive logarithmiceffect on a company's commercial SoSW. The financial stress scorepercentile has a negative linear effect on a company's commercial SoSW.

The commercial SoSW model was validated based on actual data fromhigh-balance re-underwritten OSBN accounts. FIG. 27 is a graph comparingactual commercial SoSW results to the predicted commercial SoSWestimates. As shown in FIG. 27, this model performs well as arank-ordering tool.

FIG. 28 is a Lorenz-curve graph comparing the commercial SoSW model to aperfectly random prediction. As shown in FIG. 28, the top 10% ofbusinesses, in terms of predicted commercial SoSW, account for nearly60% of the actual commercial SoSW.

In the data discussed above, the financial statements used were only forhigh-balance customers, resulting in sample selection bias. Nonetheless,the model assessment shows that this application is effective onbusinesses with annual revenue of $1 million or greater, based on Dun &Bradstreet data. This is a high-revenue segment, and approximately 12%to 15% of the OSBN base meets this high-revenue status. Although theexamples incorporated herein refer to this high-revenue segment, one ofskill in the pertinent art will recognize that a commercial SoSW metricmay also be developed for middle-market corporate consumers withoutdeparting from the spirit and scope of the present invention, as will bediscussed below.

Predicted commercial SoSW values are quintiled into the followingranges:

-   -   Q1: <$3.85 MM    -   Q2: $3.85 MM to $5.18 MM    -   Q3: $5.18 MM to $6.62 MM    -   Q4: $6.62 MM to $9.38 MM    -   Q5: >$9.38 MM        Although five classifications having the above values are        referred to herein, one of skill in the pertinent art will        recognize that fewer or more classifications may be used, and        the classifications may use a different range of values, without        departing from the spirit and scope of the present invention.

FIG. 29 is a chart illustrating the customer-level relationshipclassifications, or quintiles. Each quintile is separated intopercentages of customers who only charge, only lend, and both charge andlend. As shown, the proportion of OSBN charge customers increases withthe predicted commercial SoSW quintile. However, as shown in FIG. 30,which illustrates the active number of OSBN accounts by quintile, theproportion of charge customers does not necessarily increase for averageactive number of OSBN accounts by quintile.

The commercial SoSW model may output a scored output file. FIG. 31 is atable that displays customer counts in the scored output file. Customersin the higher SoSW and lower OSBN Spend cells represent the greatestpotential for converting plastic spend outside of a financial company tospend related to the financial company, as well as for convertingnon-plastic business spend to spend related to the financial company.Higher SoSW and higher OSBN Spend cells signify opportunities forgrowing OSBN spend among higher-spending customers.

As discussed above, commercial SoW for an OSBN company can be determinedbased on annual OSBN spend and commercial SoSW. Various targets andpredictors may be used to determine commercial SoW for differentcommercial segments including and other than the OSBN segment. Forexample, for OSBN companies having a revenue above $1 million asreported, for example, by Dun & Bradstreet, the commercial SoW modeltargets company financial statements using Dun & Bradstreet's CreditScoring Attribute Database (“CSAD”) as a predictor. A method ofsegmentation based on data availability and ordinary least squares(“OLS”) models can be used to output a company-level SoW value, whichcan be used for example, to analyze prospects, new accounts, andcustomer management.

For OSBN companies with an Equifax SBE trade level balance history, thecommercial SoW model may target SBE time series balance amounts usingEquifax SBE as a predictor. A methodology similar to the consumer SoWmodel can be used to output a company-level SoW value, which can beused, for example, to analyze new accounts and customer management.

For core OSBN companies, a “bottoms up” approach may be used. Tradelevel detail on commercial bureaus and other external data sources maybe targeted using the Dun & Bradstreet CSAD, Dun & Bradstreet DetailedTrade, Experian BIS, and Equifax SEE databases as predictors. A methodof segmentation based on data availability and OLS models can be used tooutput a company-level SoW value, which can be used, for example, toanalyze prospects, new accounts, and customer management.

For core OSBN companies, an industry inference approach may also beused. Industry-level financial statement data is targeted using the Dun& Bradstreet CSAD, Dun & Bradstreet Detailed Trade, Experian BIS, andEquifax SEE databases as predictors. A method of segmentation based ondata availability and OLS models can be used to output an industry-levelSoW or a company-level SoW value, which can be used, for example, toanalyze prospects, new accounts, and customer management.

For low revenue middle market companies, or for medium and largerrevenue middle market companies, company financial statements may betargeted using the Dun & Bradstreet CSAD as a predictor. The existingOSBN model is combined with new middle market data to output anindustry-level SoW or a company-level SoW value, which can be used, forexample, to analyze prospects, new accounts, and customer management.

For other middle market companies, a “bottoms up” approach may be used.Trade level detail on commercial bureaus and other external data sourcesis targeted using the Dun & Bradstreet CSAD as a predictor. A method ofsegmentation based on data availability and OLS models can be used tooutput an industry-level SoW or a company-level SoW value, which can beused, for example, to analyze prospects, new accounts, and customermanagement.

For Global Establishment Services (“GES”) companies that overlap to themiddle market or OSBN, the middle market or OSBN value can be targetedusing the middle market or OSBN data plus any unique GES data aspredictors. A method of segmentation based on data availability and OLSmodels can be used to output a company-level SoW value, which can beused, for example, to analyze prospects, new accounts, and customermanagement.

For GES companies that do not overlap with the middle market or OSBN,charge volume plus Dun & Bradstreet data and other external data may betargeted using the GES and Dun & Bradstreet as predictors. A method ofsegmentation based on data availability and OLS models can be used tooutput a company-level SoW value, which can be used, for example, toanalyze prospects, new accounts, and customer management. It can also beused to output total business volume at a company-specific level andtotal business volume at an industry-specific level.

Other data elements can be generated as well, such as a transactor vs.revolver indicator, largest transactor balance data, largest revolverbalance data, and trade types and number of trade types data. Titus,commercial SoW, including plasticable SoW (spend that can be convertedto plastic) and plastic SoW (spend that is already on plastic) can bepredicted for a wide range of companies and industries.

IV. APPLICABLE MARKET SEGMENTS/INDUSTRIES FOR SoW

Outputs of the SoW model can be used in any business or market segmentthat extends credit or otherwise needs to evaluate the creditworthinessor spend capacity of a particular customer. These businesses will bereferred to herein as falling into one of three categories: financialservices companies, retail companies, and other companies. Although theapplicable market segments and industries will be referred to hereinwith reference to consumers and individual consumer SoW, one of skill inthe art will recognize that companies and commercial SoW may be used ina similar manner without departing from the spirit and scope of thepresent invention.

The business cycle in each category may be divided into three phases:acquisition, retention, and disposal. The acquisition phase occurs whena business is attempting to gain new customers. This includes, forexample and without limitation, targeted marketing, determining whatproducts or services to offer a customer, deciding whether to lend to aparticular customer and what the line size or loan should be, anddeciding whether to buy a particular loan. The retention phase occursafter a customer is already associated with the business. In theretention phase, the business interests shift to managing the customerrelationship through, for example, consideration of risk, determinationof credit lines, cross-sell opportunities, increasing business from thatcustomer, and increasing the company's assets under management. Thedisposal phase is entered when a business wishes to dissociate itselffrom a customer or otherwise end the customer relationship. This canoccur, for example, through settlement offers, collections, and sale ofdefaulted or near-default loans.

A. Financial Services Companies

Financial services companies include, for example and withoutlimitation: banks and lenders, mutual fund companies, financiers ofleases and sales, life insurance companies, online brokerages, and loanbuyers.

Banks and lenders can utilize the SoW model in all phases of thebusiness cycle. One exemplary use is in relation to home equity loansand the rating given to a particular bond issue in the capital market.Although not specifically discussed herein, the SoW model would apply tohome equity lines of credit and automobile loans in a similar manner.

If the holder of a home equity loan, for example, borrows from thecapital market, the loan holder issues asset-backed securities (“ABS”),or bonds, which are backed by receivables. The loan holder is thus anABS issuer. The ABS issuer applies for an ABS rating, which is assignedbased on the credit quality of the underlying receivables. One of skillin the art will recognize that the ABS issuer may apply for the ABSrating through any application means without altering the spirit andscope of the present invention. In assigning a rating, the ratingagencies weigh a loads probability of default by considering thelender's underwriting and portfolio management processes. Lendersgenerally secure higher ratings by credit enhancement. Examples ofcredit enhancement include over collateralization, buying insurance(such as wrap insurance), and structuring ABS (through, for example,senior/subordinate bond structures, sequential pay vs, pari passu, etc.)to achieve higher ratings. Lenders and rating agencies take theprobability of default into consideration when determining theappropriate level of credit enhancement.

During the acquisition phase of a loan, lenders may use the SoW model toimprove their lending decisions. Before issuing the loan, lenders canevaluate a consumer's spend capacity for making payments on the loan.This leads to fewer had loans and a reduced probability of default forloans in the lender's portfolio. A lower probability of default meansthat, for a given loan portfolio that has been originated using the SOWmodel, either a higher rating can be obtained with the same degree ofover-collateralization, or the degree of over-collateralization can bereduced for a given debt rating. Thus, using the SoW model at theacquisition stage of the loan reduces the lender's overall borrowingcost and loan loss reserves.

During the retention phase of a loan, the SoW model can be used to tracka customer's spend. Based on the SOW outputs, the lender can makevarious decisions regarding the customer relationship. For example, alender may use the SoW model to identify borrowers who are in financialdifficulty. The credit lines of those borrowers which have not fullybeen drawn down can then be reduced. Selectively revoking unused linesof credit may reduce the probability of default for loans in a givenportfolio and reduce the lender's borrowing costs. Selectively revokingunused lines of credit may also reduce the lender's risk by minimizingfurther exposure to a borrower that may already be in financialdistress.

During the disposal phase of a loan, the SoW model enables lenders tobetter predict the likelihood that a borrower will default. Once thelender has identified customers who are in danger of default, the lendermay select those likely to repay and extend settlement offers.Additionally, lenders can use the SoW model to identify which customersare unlikely to pay and those who are otherwise not worth extending asettlement offer.

The SoW model allows lenders to identify loans with risk of default,allowing lenders, prior to default, to begin anticipating a course ofaction to take if default occurs. Because freshly defaulted loans fetcha higher sale price than loans that have been non-performing for longertime periods, lenders may sell these loans earlier in the defaultperiod, thereby reducing the lender's costs.

The ability to predict and manage risk before default results in a lowerlikelihood of default for loans in the lender's portfolio. Further,even, in the event of a defaulted loan, the lender can detect thedefault early and thereby recoup a higher percentage of the value ofthat loan. A lender using the SoW model can thus show to the ratingagencies that it uses a combination of tight underwriting criteria androbust post-lending portfolio management processes. This enables thelender to increase the ratings of the ABS that are backed by a givenpool or portfolio of loans and/or reduce the level ofover-collateralization or credit enhancement required in order to obtaina particular rating.

Turning to mutual funds, the SOW model may be used to manage therelationship with customers who interact directly with the company.During the retention phase, if the mutual fund company concludes that acustomer's spending capacity has increased, the company can concludethat either or both of the customer's discretionary and disposableincome has increased. The company can then market additional funds tothe customer. The company can also cross-sell other services that thecustomer's increased spend capacity would support.

Financiers of leases or sales, such as automobile lease or salefinanciers, can benefit from SoW outputs in much the same way as a bankor lender, as discussed above. In typical product financing, however,the amount of the loan or lease is based on the value of the productbeing financed. Therefore, there is generally no credit limit that needsto be revisited during the course of the loan. For this reason, the SoWmodel is most useful to lease/sales finance companies during theacquisition and disposal phases of the business cycle.

Life insurance companies can primarily benefit from the SoW model duringthe acquisition and retention phases of the business cycle. During theacquisition phase, the SoW model allows insurance companies to identifythose people with adequate spend capacity for paying premiums. Thisallows the insurance company to selectively target its marketing effortsto those most likely to purchase life insurance. For example, theinsurance company could model consumer behavior in a similar manner asthe “best customer” model described above. During the retention phase,an insurance company can use the SoW model to determine which of itsexisting clients have increased their spend capacity and would have agreater capability to purchase additional life insurance. In this way,those existing customers could be targeted at a time during which theywould most likely be willing to purchase without overloading them withmaterials when they are not likely to purchase.

The SoW model is most relevant to brokerage and wealth managementcompanies during the retention phase of the business cycle. Due toconvenience factors, consumers typically trade through primarily onebrokerage house. The more incentives extended to a customer by acompany, the more likely the customer will use that company for themajority of its trades. A brokerage house may thus use the SoW model todetermine the capacity or trend of a particular customer's spend andthen use that data to cross-sell other products and/or as the basis foran incentive program. For example, based on the SoW outputs, aparticular customer may become eligible for additional services offeredby the brokerage house, such as financial planning, wealth management,and estate planning services.

Just as the SoW model can help loan holders determine that a particularloan is nearing default, loan buyers can use the model to evaluate thequality of a prospective purchase during the acquisition phase of thebusiness cycle. This assists the loan buyers in avoiding or reducing thesale prices of loans that are in likelihood of default.

B. Retail Companies

Aspects of the retail industry for which the SoW model would beadvantageous include, for example and without limitation: retail storeshaving private label cards, on-line retailers, and mail order companies.

There are two general types of credit and charge cards in themarketplace today: multipurpose cards and private label cards. A thirdtype of hybrid card is emerging. Multipurpose cards are cards that canbe used at multiple different merchants and service providers. Forexample, American Express, Visa, Mastercard, and Discover are consideredmultipurpose card issuers. Multipurpose cards are accepted by merchantsand other service providers in what is often referred to as an “opennetwork.” This essentially means that transactions are routed from apoint-of-sale (“PUS”) through a network for authorization, transactionposting, and settlement. A variety of intermediaries play differentroles in the process. These include merchant processors, the brandnetworks, and issuer processors. This open network is often referred toas an interchange network. Multipurpose cards include a range ofdifferent card types, such as charge cards, revolving cards, and debitcards, which are linked to a consumer's demand deposit account (“DDA”)or checking account.

Private label cards are cards that can be used for the purchase of goodsand services from a single merchant or service provider. Historically,major department stores were the originators of this type of card.Private label cards are now offered by a wide range of retailers andother service providers. These cards are generally processed on a closednetwork, with transactions flowing between the merchant's POS and itsown backoffice or the processing center for a third-party processor.These transactions do not flow through an interchange network and arenot subject to interchange fees.

Recently, a type of hybrid card has evolved. This is a card that, whenused at a particular merchant, is that merchant's private label card,but when used elsewhere, becomes a multipurpose card. The particularmerchant's transactions are processed in the proprietary private labelnetwork. Transactions made with the card at all other merchants andservice providers are processed through an interchange network.

Private label card issuers, in addition to multipurpose card issuers andhybrid card issuers, can apply the SoW model in a similar way asdescribed above with respect to credit card companies. That is,knowledge of a consumer's spend capability, as well as knowledge of theother SoW outputs, could be used by card issuers to improve performanceand profitability across the entire business cycle.

Online retail and mail order companies can use the SoW model in both theacquisition and retention phases of the business cycle. During theacquisition phase, for example, the companies can base targetedmarketing strategies on SoW outputs. This could substantially reducecosts, especially in the mail order industry, where catalogs aretypically sent to a wide variety of individuals. During the retentionphase, companies can, for example, base cross-sell strategies or creditline extensions on SoW outputs.

The SoW model may also be useful to merchants accepting checks at apoint of sale (“POS”). Before accepting a check from a consumer at a POSas a form of payment, merchants typically “verify” the check or requesta “check guarantee”. The verification and/or guarantee are usuallyprovided by outside service providers.

Verification reduces the risk of the merchant's accepting a bad check.When a consumer attempts to pay by check, the merchant usually asks fora piece of identification. The merchant then forwards details of thecheck, such as the MICR number, and details of the identification (e.g.,a driver's license number if the driver's license is proffered asidentification) to a service provider. On a per transaction basis, theservice provider searches one or more databases (e.g., National CheckNetwork) containing negative and positive check writer accounts. Theservice provider uses these accounts to determine if there is a matchbetween information in the database(s) and the specific piece ofinformation provided by the merchant. A match may identify whether thecheck writer has a positive record or delinquent check-related debts.

Upon notification of this match, the merchant decides whether to acceptor decline the check. The notification may be provided, for example, viaa coded response from the provider. If the service provider is not acheck guarantor, there is no guarantee that the check will be honored bythe check writer's bank even when a search of the database(s) does notresult in any negative results. The service providers earn a transactionfee each time the databases are searched.

Under a check guarantee arrangement, however, the service providerguarantees a check to the merchant. If the check is subsequentlydishonored by the customer's bank, the merchant is reimbursed by theservice provider, which then acquires rights to collect the delinquentamount from the check writer. The principal risk of providing thisservice is the risk of ever collecting the amount that the serviceprovider guaranteed from a delinquent check writer whose check wasdishonored by his hank. If the service provider is unable to collect theamount, it loses that amount.

Before guaranteeing a check, the service provider searches severaldatabases using the customer data supplied by the merchant. The serviceprovider then scores each transaction according to several factors.Factors which may be considered include, for example and withoutlimitation, velocity, prior activity, check writer's presence in otherdatabases, size of the check, and prior had check activity by geographicand/or merchant specific locations. Velocity is the number of times acheck writer has been searched in a certain period of time. Prioractivity is based on the prior negative or positive transactions withthe check writer. Check writer's presence in other databases looks atnational databases that are selectively searched based on the size ofthe check and prior activity with the check writer. If the scoringsystem concludes that the risk is too high, the service provider refusesto guarantee the check. If the scoring system provides a positiveresult, the service provider agrees to guarantee the check.

Use of the SoW model thus benefits the service providers. At theorigination phase, service providers may use SoW scores as one of theparameters for deciding whether or not to guarantee a check. Forexample, the SoW score can be used to differentiate between a low-riskconsumer and a high-risk consumer. A low-risk consumer may be, forexample, a person who is writing more checks because his income, asdetermined by the SoW model, has probably increased. In this case, thecheck velocity is not necessarily a measurement of higher risk. Ahigh-risk consumer, on the other hand, may be a person whose checkvelocity has increased without a corresponding increase in income orspend capacity, as shown by the SoW model.

On average, some service providers collect on only 50% to 60% of thecheeks that they guarantee and that subsequently become delinquent. Atthe disposal phase of the business cycle, the service providers may usethe SoW model in a similar manner to other financial institutions, asdescribed above. For example, service providers may use SoW todetermine, for example, which debts to collect in-house and which debtsto sell. Thus, SoW helps service providers make the collection processmore efficient.

C. Other Companies

Types of companies which also may make use of the SoW model include, forexample and without limitation: the gaming industry, charities anduniversities, communications providers, hospitals, and the travelindustry.

The gaming industry can use the SoW model in, for example, theacquisition and retention phases of the business cycle. Casinos oftenextend credit to their wealthiest and/or most active players, also knownas “high rollers.” The casinos can use the SoW model in the acquisitionphase to determine Whether credit should be extended to an individual.Once credit has been extended, the casinos can use the SoW model toperiodically review the customer's spend capacity. If there is a changein the spend capacity, the casinos may alter the customer's credit lineto be more commensurate with the customer's spend capacity.

Charities and universities rely heavily on donations and gifts. The SoWmodel allows charities and universities to use their often limitedresources more effectively by timing their solicitations to coincidewith periods when donors have had an increase indisposable/discretionary income and are thus better able to makedonations. The SoW model also allows charities and universities toreview existing donors to determine whether they should be targeted foradditional support.

Communications providers, such as telephone service providers oftencontract into service plans with their customers. In addition toimproving their targeted marketing strategies, communications providerscan use the SoW outputs during the acquisition phase to determinewhether a potential customer is capable of paying for the service underthe contract.

The SoW model is most applicable to hospitals during the disposal phaseof the business cycle. Hospitals typically do not get to choose ormanage the relationship with their patients. Therefore, they are oftenin the position of trying to collect for their services from patientswith whom there was no prior customer relationship. There are two waysthat a hospital can collect its fees. The hospital may run thecollection in-house, or the hospital may turn over responsibility forthe collection to a collection agent. Although the collection agentoften takes fees for such a service, it can be to the hospital's benefitif the collection is time-consuming and/or difficult.

The SoW model can be used to predict which accounts are likely to paywith minimal persuasion, and which ones are not. The hospital can thenselect which accounts to collect in-house, and which accounts tooutsource to collection agencies. For those that are retained in-house,the hospital can further segment the accounts into those that requiresimple reminders and those requiring more attention. This allows thehospital to optimize the use of its in-house collections staff. Byselectively outsourcing collections, the hospital and other lendersreduces the contingency fees that it pays to collection agencies, andmaximizes the amount collected by the in-house collection team.

Members of the travel industry can make use of the SoW data in theacquisition and retention stages of the business cycle. For example, ahotelier typically has a brand of hotel that is associated with aparticular “star-level” or class of hotel. In order to capture variousmarket segments, hoteliers may be associated with several hotel brandsthat are of different classes. During the acquisition phase of thebusiness cycle, a hotelier may use the SoW method to target individualsthat have appropriate spend capacities for various classes of hotels.During the retention phase, the hotelier may use the SoW method todetermine, for example, when a particular individual's spend capacityincreases. Based on that determination, the hotelier can market a higherclass of hotel to the consumer in an attempt to convince the consumer toupgrade.

One of skill in the relevant art(s) will recognize that many of theabove-described SoW applications may be utilized by other industries andmarket segments without departing from the spirit and scope of thepresent invention. For example, the strategy of using SoW to model anindustry's “best customer” and targeting individuals sharingcharacteristics of that best customer can be applied to nearly allindustries.

SoW data can also be used across nearly all industries to improvecustomer loyalty by reducing the number of payment reminders sent toresponsible accounts. Responsible accounts are those who are most likelyto pay even without being contacted by a collector. The reduction inreminders may increase customer loyalty, because the customer will notfeel that the lender or service provider is unduly aggressive. Thelender's or service provider's collection costs are also reduced, andresources are freed to dedicate to accounts requiring more persuasion.

Additionally, the SoW model may be used in any company having a largecustomer service call center to identify specific types of customers.Transcripts are typically made for any call from a customer to a callcenter. These transcripts may be scanned for specific keywords ortopics, and combined with the SoW model to determine the consumer'scharacteristics. For example, a bank having a large customer servicecenter may scan service calls for discussions involving bankruptcy. Thebank could then use the SoW model with the indications from the callcenter transcripts to evaluate the customer.

V. APPLICABLE MARKET SEGMENTS/INDUSTRIES FOR COMMERCIAL SoW ANDCOMMERCIAL SoSW

A. Banks, Lenders, and Credit Providers

Banks, lenders, and credit providers (referred to collectively herein as“lenders”) lend money based on a borrower's credit rating andcollateral. Even when loans are secured by collateral, though, there isno guarantee that the value of the collateral will not depreciate overtime to a value that is below the outstanding loan balance. While acredit rating of the borrower may be a good indicator of a borrower'swillingness to repay, it is not a good indicator of borrower's futureability to repay. By predicting future spend, the commercial SoW andcommercial SoSW models provide a score that is, effectively, a proxy forpredicting a borrowers ability to repay.

In the acquisition stage of the customer lifecycle, lenders can usecommercial SoW and/or commercial SoSW models to determine to whom theyshould lend, and to whom they should deny credit. The commercial modelsmay also be used for pricing loans and other products in a dynamic way.By using the commercial models to determine whose profits and/or spendis likely to increase, for example, lenders can use the scores producedby the commercial models as search criteria to identify which existingcustomers should be targeted for both new and existing products. Thescores may also be used to identify companies who are not yet clientswho could be targeted for lender products.

In the retention stage of the customer lifecycle, lenders can use thecommercial models to determine which customers should be retained. Themodels can also be used to segment existing customers for cross-sellingpurposes. Additionally, the models can be used to manage credit riskand/or exposure from existing loans. For example, if the commercialmodels predict that a business is undergoing or will undergo increasedfinancial stress and/or credit risk, the lender could revoke thebusiness's unused lines of credit.

In the disposal stage, the commercial models can be used to determinewhich customers Should be extended settlement offers by the lender. Thelender can also use the commercial models to identify which businessloans are likely to default. The lender can thus sell these loansearly-on to get a higher sale price. This is useful since the loanseller gets fewer cents on the dollar as the time that lapses betweenloan default and sale grows longer. The lender can also use thecommercial models to determine which loans should be collected in-house,and which loans should be sent out to collection agencies.

B. Investment Vehicles and Investment Vehicle Managers

Although mutual funds will be used herein as example investmentvehicles, one of skill in the relevant art(s) will recognize thatcommercial SoW and commercial SoSW can benefit many other types ofinvestment vehicles, such as hedge funds.

Mutual funds, for example, that invest using a so-called “top-down”approach identify stocks by first selecting industries that matchcertain criteria, and then zeroing in on companies in that industry thatmatch other criteria. The other criteria may be, for example and withoutlimitation, size, revenue growth, profits, price-earnings ratios, andrevenue growth vs. expense growth. Funds that use a so-called“bottom-up” approach identify securities by zeroing in on companies thatmatch specific criteria, without starting at the industry level. Somemanagers also use analyst reviews and credit agency reports, among otherdevices. Whether using a top-down approach, a bottom-up approach, or acombination of both, the fund managers rely on historical data. Thesedata tend to be disjointed and are not often connected.

The commercial SoW and/or commercial SoSW models may be used to presentfund managers with a simple yet robust score, which is a quantitativemeasure that indicates whether or not a company is expected to do well.This score may be of particular interest if the mutual fund is about tobuy securities of the company. Typically, investors and fund managersuse historical information. When they invest, they assume that ahistorical trend will continue. That is, they frequently assume that acompany will continue to be profitable. However, funds and otherinvestors, particularly those that invest in smaller companies, do notalways have access to reliable and accurate historical data and to asingle score that encapsulates a company's revenues, expenses, andfinancial stress. The commercial models provide a score that encompassesall of these.

In the acquisition stage of the customer lifecycle, mutual funds can usea score produced by the commercial models as one of the parameters to beconsidered when picking stocks and when determining which stocks to buy,sell, or short.

The commercial models may also be used in the retention and disposalstages. After buying stocks, money managers normally set a price targetat which to sell. The stocks are sold once the price reaches thatpre-set level. Alternatively, if it seems that the price will neverreach that preset level or prices fall instead of rising as expected,the stock may be sold at a loss. Fund managers can use the commercialmodels to predict which stocks in their portfolio are likely to suffer aprice fall.

In an example scenario, a mutual fund has purchased the securities of acompany. The company sells its products to other companies in a certainindustry. The mutual fund could use scores produced by the commercialmodels to predict whether or not the company's customers will bespending less in the future, thus reducing the company's revenues andpossibly its share price. In addition, if a particular customer is oneof the company's major customers, the mutual fund could use scoresproduced by the commercial model to determine and/or predict potentialfinancial trouble at the particular customer. With such knowledge, themutual fund could sell the company's shares before the price plummets.Alternatively, if the scores produced by the commercial models show thatthe particular customer will be doing better, the mutual fund could buymore shares of the company.

C. Research Analysts

A research analyst provides a rating that summarizes the analyst'sopinion about the quality and/or prospects of the rated company'ssecurities. Such a rating might be “BUY,” “HOLD,” or “SELL” for equity,or “A,” “B,” “C,” or “JUNK” for debt. Whether conducting analyses thatwould result in a rating for debt or equity, analysts review a company'sperformance, management and prospects, among other things.

While it is standard practice for rated companies to provide analystswith factual historical data, the clients of such rated companies haveno obligation to give the analyst any data unless the client is alsorated by the same analyst. In the absence of such information, theanalysts projections about the future prospects of the rated company,and any rating that is based on such projections, is pure speculation.

With the commercial SoW and/or commercial SoSW models, however, theanalyst has a simple, yet comprehensive, indication of the businessprospects of the customers of the rated company. With scores produced bythe commercial models, therefore, the analyst is then able to provide amuch more meaningful rating that provides a more accurate picture of therated company.

As an example, an analyst follows a particular corporation. He alsorates the securities issued by the corporation. The main customers ofthe corporation are companies in a specific industry. The corporationhas issued some bonds, and plans to service those bonds with therevenues from selling to customers in the specific industry, in thisscenario, which is not unique, the analyst could have access to publichistorical financial information from some companies in the specificindustry. These historical data, however, are not forward-looking, anddo not tell the analyst the prospects of the companies in the specificindustry.

However, with scores produced by the commercial models, the analyst canpredict whether or not the companies in the specific industry intend toincrease or decrease their spend. Thus, by combining the predictivecapabilities of the commercial models and the analyst's knowledge of thecorporation, the analyst can issue a much more accurate and reliablerating for the securities issued by the corporation. The analyst is ableto use scores produced by the commercial models to assign new ratingsand change existing ratings.

D. Government Agencies, Procurement Departments, and Others thatPatronize Small Businesses

Government departments and agencies and large publicly traded firms areusually obliged by law or otherwise to patronize small businesses. Suchpatronage takes various forms, including, for example and withoutlimitation, so-called 8(a) programs, small business set aside programs,and disadvantaged business entity programs. Once certified, a smallbusiness can bid as a sole source provider for government contractsworth several million dollars.

Certifying agencies rely on Dun & Bradstreet scores and an array ofself-reported data to certify a business as, for example and withoutlimitation, small, woman-owned, minority-owned, or a disadvantagedbusiness entity. To be certified as a woman-owned business, for example,the certifying authority basically certifies that the business is atleast 51% owned by one or more women. Such self-reported data, even whenaccurate, are only required to be updated every year or so. Further,these data do not have the inherent capability to provide an indicationof whether the particular small business is growing or shrinking, orwhether the particular industry served by such small business (the smallbusiness's revenue source) is growing or shrinking.

Thus, while such certifications might level the playing field by givingsmall businesses access to opportunities they might not otherwise have,they also put those buying the services (the government agencies,procurement departments, etc.) at risk. This is because most smallbusinesses fail within the first few years, and small-business typecertifications do not provide an indication of the likelihood that aparticular business would continue as a going concern.

By using the commercial SoW and/or commercial SoSW models, buyers ofservices can determine, before awarding and/or renewing contracts,whether the vendor is on the upswing or on its last breath. Such servicebuyers could also use a combination of the commercial models andstatistical analyses to predict the likelihood that a particular smallbusiness will remain in business.

In the acquisition stage of the customer lifecycle, the agency orprocurement department can use the commercial models to determine towhom contracts should be awarded, and to whom business should be denied.Further, to the extent that service buyers require vendors that aresmall businesses to post performance bonds, such service buyers couldalso use the commercial models to determine whether or not a performancebond should be required and, if so, the amount the performance bondshould be. In addition to using the commercial models as tools fordetermining to whom contracts should be awarded, such service buyers,when appropriate, can use scores produced by the commercial models toprepare a shortlist of who to solicit proposals from. This may occur,for example, when sending out requests for proposals that are notbroadcast to everyone.

In the retention stage, agencies or procurement departments can usescores produced by the commercial models to manage their approved vendorlists. In the disposal stage, they can use scores produced by thecommercial models to proactively determine which vendors to remove fromtheir approved vendor lists.

E. Insurance Companies

Insurance companies sell businesses a product called “key maninsurance.” Basically, key man insurance is a life insurance policy onthe key/crucial/critical people in a business. In a small business, thisis usually the owner, the founder(s), or perhaps a key employee or two(all collectively referred to herein as key employee(s)). If somethingwere to happen to these people, the business would most probably sink.With key man term life insurance, a company purchasing a life insurancepolicy on the key employee(s) pays the premiums. That company becomesthe beneficiary of the policy. If the key employee(s) dies suddenly, thecompany receives the insurance payoff. In effect, the key man insurancehelps the insured company to mitigate the adverse impact of losing thekey employee(s). The company can use the insurance proceeds for expensesuntil it hires a replacement, or, if necessary, settle debts, distributemoney to stakeholders, provide severance packages, and wind down thebusiness in an orderly manner.

To price such insurance policies, insurers rely on an array of data,including the insured company's historical financials. Some insurersmight even go as far as analyzing the industry that constitutes thecustomer base (and thus revenue source) of the company buying key maninsurance. Such analyses, however, tend to be general at best. Inaddition, even if the insurance company wants to analyze the businessprospects of the insured company's particular customers, such customersare not obligated to provide any data, let alone accurate data, to theinsurance company. Consequently, insurers face significant danger ofunderpricing risk. In extreme cases, this information asymmetry resultsin outright fraud against the insurers.

With the commercial SoW and/or commercial SoSW models, insurers canreduce the danger of underpricing risk, and thus price their riskaccordingly. For example, when pricing a key man policy, the insurer canask the insured for a list of its major customers in addition toanalyzing the historical financials of the insured company. With such alist, the insurer can then factor into its premium calculations thebusiness prospects of each such customer. In extreme cases, the insurercould even refuse to provide key man insurance to a company, because itmay not be reasonable to provide insurance to a company that is about togo under.

In the acquisition stage of the customer lifecycle, insurance companiescan use the commercial models to decide whether or not to sell insuranceto a particular company. The commercial models can also be used as afactor in determining what the insurance should be. Additionally, thecommercial models can be used by the insurance company as a filter foridentifying prospective clients.

In the retention stage, insurance companies can use the commercialmodels as a factor to decide whether to re-price the premium on apolicy, and also to decide whether to increase or decrease the payoutamount for a particular premium. In the disposal stage, insurancecompanies can use the commercial models to decide when to revoke theinsurance policy for a particular client.

VI. SYSTEM IMPLEMENTATIONS

The present invention may be implemented using hardware, software or acombination thereof and may be implemented in one or more computersystems or other processing systems. However, the manipulationsperformed by the present invention were often referred to in terms, suchas adding or comparing, which are commonly associated with mentaloperations performed by a human operator. No such capability of a humanoperator is necessary, or desirable in most cases, in any of theoperations described herein which form part of the present invention.Rather, the operations are machine operations. Useful machines forperforming the operation of the present invention include generalpurpose digital computers or similar devices.

In fact, in one embodiment, the invention is directed toward one or morecomputer systems capable of carrying out the functionality describedherein. An example of a computer system 3200 is shown in FIG. 32.

The computer system 3200 includes one or more processors, such asprocessor 3204. The processor 3204 is connected to a communicationinfrastructure 3206 (e.g., a communications bus, cross-over bar, ornetwork). Various software embodiments are described in terms of thisexemplary computer system. After reading this description, it willbecome apparent to a person skilled in the relevant art(s) how toimplement the invention using other computer systems and/orarchitectures.

Computer system 3200 can include a display interface 3202 that forwardsgraphics, text, and other data from the communication infrastructure3206 (or from a frame buffer not shown) for display on the display unit3230.

Computer system 3200 also includes a main memory 3208, preferably randomaccess memory (RAM), and may also include a secondary memory 3210. Thesecondary memory 3210 may include, for example, a hard disk drive 3212and/or a removable storage drive 3214, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc. The removable storagedrive 3214 reads from and/or writes to a removable storage unit 3218 ina well known manner. Removable storage unit 3218 represents a floppydisk, magnetic tape, optical disk, etc. which is read by and written toby removable storage drive 3214. As will be appreciated, the removablestorage unit 3218 includes a computer usable storage medium havingstored therein computer software and/or data.

In alternative embodiments, secondary memory 3210 may include othersimilar devices for allowing computer programs or other instructions tobe loaded into computer system 3200. Such devices may include, forexample, a removable storage unit 3218 and an interface 3220. Examplesof such may include a program cartridge and cartridge interface (such asthat found in video game devices), a removable memory chip (such as anerasable programmable read only memory (EPROM), or programmable readonly memory (PROM)) and associated socket, and other removable storageunits 3218 and interfaces 3220, which allow software and data to betransferred from the removable storage unit 3218 to computer system3200.

Computer system 3200 may also include a communications interface 3224.Communications interface 3224 allows software and data to be transferredbetween computer system 3200 and external devices. Examples ofcommunications interface 3224 may include a modem, a network interface(such as an Ethernet card), a communications port, a Personal ComputerMemory Card International Association (PCMCIA) slot and card, etc.Software and data transferred via communications interface 3224 are inthe form of signals 3228 which may be electronic, electromagnetic,optical or other signals capable of being received by communicationsinterface 3224. These signals 3228 are provided to communicationsinterface 3224 via a communications path (e.g., channel) 3226. Thischannel 3226 carries signals 3228 and may be implemented using wire orcable, fiber optics, a telephone line, a cellular link, a radiofrequency (RE) link and other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage drive 3214, a hard disk installed in hard disk drive 3212, andsignals 3228. These computer program products provide software tocomputer system 3200. The invention is directed to such computer programproducts.

Computer programs (also referred to as computer control logic) arestored in main memory 3208 and/or secondary memory 3210. Computerprograms may also be received via communications interface 3224. Suchcomputer programs, when executed, enable the computer system 3200 toperform the features of the present invention, as discussed herein. Inparticular, the computer programs, when executed, enable the processor3204 to perform the features of the present invention. Accordingly, suchcomputer programs represent controllers of the computer system 3200.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intocomputer system 3200 using removable storage drive 3214, hard drive 3212or communications interface 3224. The control logic (software), whenexecuted by the processor 3204, causes the processor 3204 to perform thefunctions of the invention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine so as to perform the functions described herein will beapparent to persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

VII. CONCLUSION

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It will be apparent to persons skilled inthe relevant art(s) that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentinvention. Thus, the present invention should not be limited by any ofthe above described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

In addition, it should be understood that the figures and screen shotsillustrated in the attachments, which highlight the functionality andadvantages of the present invention, are presented for example purposesonly. The architecture of the present invention is sufficiently flexibleand configurable, such that it may be utilized (and navigated) in waysother than that shown in the accompanying figures.

Further, the purpose of the foregoing Abstract is to enable the U.S.Patent and Trademark Office and the public generally, and especially thescientists, engineers and practitioners in the art who are not familiarwith patent or legal terms or phraseology, to determine quickly from acursory inspection the nature and essence of the technical disclosure ofthe application. The Abstract is not intended to be limiting as to thescope of the present invention in any way.

1. A method of determining insurance risk, comprising: modeling, by acomputer-based system for determining said insurance risk comprising acomputer processor and a tangible, non-transitory memory, industryspending patterns using individual corporate data and aggregatecorporate data, including financial statement data, wherein the modelingcomprises: identifying the cost of goods sold and operating expensesfrom at least one of the individual corporate data and the aggregatecorporate data; at least one of identifying the cardable operatingexpenses from the operating expenses and identifying a cardableoperating expenses ratio from aggregate corporate data; at least one ofsumming the cardable operating expenses with the cost of goods sold andsumming the cost of goods sold with the product of operating expensesand the cardable operating expenses ratio; estimating, by thecomputer-based system, a commercial size of spending wallet of a companybased on, at least, known financial statement data of the company, totalknown business spending of the company, and the model of industryspending patterns, wherein the commercial size of spending wallet is thetotal cardable business spending of the company, and wherein the modelof industry spending patterns is used to infer financial statement dataand spending data that is not known; and determining an insurance riskusing at least the estimated commercial size of spending wallet of thecompany.
 2. The method of claim 1, further comprising determiningwhether to sell insurance to the company based on the insurance risk. 3.The method of claim 1, further comprising determining the amount ofinsurance to sell to the company based on the insurance risk.
 4. Themethod of claim 1, further comprising determining the premium amount forinsuring the company based on the insurance risk.
 5. The method of claim1, further comprising determining a payout amount for an insurancepremium based on the insurance risk.
 6. The method of claim 1, furthercomprising determining when to revoke an insurance policy held by thecompany based on the insurance risk.
 7. The method of claim 6, whereinthe insurance is key man insurance.
 8. The method of claim 1, whereinthe estimating comprises outputting the commercial size of spendingwallet as a score.
 9. The method of claim 1, wherein the company is acustomer of a provider, and the insurance risk is determined for theprovider.
 10. A system for determining insurance risk, the systemcomprising: a non-transitory memory communicating with a processor, thenon-transitory memory having instructions stored thereon that, inresponse to execution by the processor, cause the processor to performoperations comprising: modeling, by the processor, industry spendingpatterns using individual corporate data and aggregate corporate data,including financial statement data, wherein the modeling comprises:identifying the cost of goods sold and operating expenses from at leastone of the individual corporate data and the aggregate corporate data;at least one of identifying the cardable operating expenses from theoperating expenses and identifying a cardable operating expenses ratiofrom aggregate corporate data; at least one of summing the cardableoperating expenses with the cost of goods sold and summing the cost ofgoods sold with the product of operating expenses and the cardableoperating expenses ratio; estimating, by the processor, a commercialsize of spending wallet of a company based on, at least, known financialstatement data of the company, total known business spending of thecompany, and the model of industry spending patterns, wherein thecommercial size of spending wallet is the total cardable businessspending of the company, and wherein the model of industry spendingpatterns is used to infer financial statement data and spending datathat is not known; and determining an insurance risk using at least theestimated commercial size of spending wallet of the company.
 11. Thesystem of claim 10, wherein the instructions stored thereon, in responseto execution by the processor, cause the processor to perform operationsfurther comprising determining whether to sell insurance to the companybased on the insurance risk.
 12. The system of claim 10, wherein theinstructions stored thereon, in response to execution by the processor,cause the processor to perform operations further comprising determiningthe amount of insurance to sell to the company based on the insurancerisk.
 13. The system of claim 10, wherein the instructions storedthereon, in response to execution by the processor, cause the processorto perform operations further comprising determining the premium amountfor insuring the company based on the insurance risk.
 14. The system ofclaim 10, wherein the instructions stored thereon, in response toexecution by the processor, cause the processor to perform operationsfurther comprising determining a payout amount for an insurance premiumbased on the insurance risk.
 15. The system of claim 10, wherein theinstructions stored thereon, in response to execution by the processor,cause the processor to perform operations further comprising.
 16. Thesystem of claim 15, wherein the insurance is key man insurance.
 17. Thesystem of claim 10, wherein the instructions stored thereon, in responseto execution by the processor, cause the processor to perform operationsfurther comprising outputting the estimated commercial size of spendingwallet as a score.
 18. An article of manufacture including a computerreadable medium having instructions stored thereon that, in response toexecution by a computing device, cause the computing device to performoperations comprising: mudding, by the computing device, industryspending patterns using individual corporate data and aggregatecorporate data, including financial statement data, wherein the modelingcomprises: identifying the cost of goods sold and operating expensesfrom at least one of the individual corporate data and the aggregatecorporate data; at least one of identifying the cardable operatingexpenses from the operating expenses and identifying a cardableoperating expenses ratio from aggregate corporate data; at least one ofsumming the cardable operating expenses with the cost of goods sold andsumming the cost of goods sold with the product of operating expensesand the cardable operating expenses ratio; estimating, by the computingdevice, a commercial size of spending wallet of a company based on, atleast, known financial statement data of the company, total knownbusiness spending of the company, and the model of industry spendingpatterns, wherein the commercial size of spending wallet is the totalcardable business spending of the company, and wherein the model ofindustry spending patterns is used to infer financial statement data andspending data that is not known; and determining an insurance risk usingat least the estimated commercial size of spending wallet of thecompany.
 19. The article of claim 18 wherein the instructions storedthereon, in response to execution by the computing device processor,cause the computing device to perform operations further comprisingdetermining whether to sell insurance to the company based on theinsurance risk.
 20. The article of claim 18, wherein the instructionsstored thereon, in response to execution by the computing device, causethe computing device to perform operations further comprisingdetermining the amount of insurance to sell to the company based on theinsurance risk.